Zobrazeno 1 - 10 of 18 pro vyhledávání: '"Mohsen Hadian"'
2
Learning-based Fractional Order PID Controller for Load Frequency Control of Distributed Energy Resources Including PV and Wind Turbine Generator
Autor: Mohsen Babaei, Mohsen Hadian
Publikováno v: Distributed Generation & Alternative Energy Journal.
Due to the ever-increasing penetration of renewable resources, Frequency control of microgrids has recently been received special consideration from researchers. The continual supply of load consumption is the major issue of standalone microgrids due
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0d8312132b1d91277127d82ed7610392
https://doi.org/10.13052/dgaej2156-3306.3762
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3
A Novel Exergy-Based Optimization Approach in Model Predictive Control for Energy-Saving Assessment
Autor: Mohammad. H. Asheri, Masoud Babaei, Karim Salahshoor, Mehdi Doostinia, Mohsen Hadian
Publikováno v: Journal of Control, Automation and Electrical Systems. 31:1481-1488
Nowadays, in many industrial applications, energy management is recognized as an essential issue. Comprehensive understanding of exergetic perspectives can help save more resources. A unique exergy-based optimization approach in model predictive cont
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::473f869132ff2a56665d6a3094f6ce60
https://doi.org/10.1007/s40313-020-00640-6
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5
A New Event-Based PI Controller Using Evolutionary Algorithms
Autor: Alireza Aarabi, Milad Mehrshadian, Mohsen Hadian, Amin Biglary Makvand
Publikováno v: Journal of Control, Automation and Electrical Systems. 30:841-849
This paper firstly tries to address the issue of high computational load of a PI controller and secondly tries to find the best method among three existing methods for tuning the PI controller. The three existing methods are Ziegler–Nichols (Z–N)
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a5acc707d8c9d48384df6460d3a330de
https://doi.org/10.1007/s40313-019-00519-1
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6
Robust Model Predictive Controller Using Recurrent Neural Networks for Input–Output Linear Parameter Varying Systems
Autor: Mohsen Hadian, Wenjun Zhang, Amin Ramezani
Publikováno v: Electronics, Vol 10, Iss 1557, p 1557 (2021)
Electronics
Volume 10
Issue 13
This paper develops a model predictive controller (MPC) for constrained nonlinear MIMO systems subjected to bounded disturbances. A linear parameter varying (LPV) model assists MPC in dealing with nonlinear dynamics. In this study, the nonlinear proc
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ded09f23d49c51edd4b7e78534e01563
https://www.mdpi.com/2079-9292/10/13/1557
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8
Application of artificial intelligence in modeling, control, and fault diagnosis
Autor: Seyed Mohammad Ebrahimi Saryazdi, Ardashir Mohammadzadeh, Masoud Babaei, Mohsen Hadian
Publikováno v: Applications of Artificial Intelligence in Process Systems Engineering ISBN: 9780128210925
Advanced control systems are becoming more and more sophisticated every day. For safety-critical systems such as chemical processes, nuclear reactors, aircraft and spacecraft, the issue of reliability, acceptable performance, and environmental protec
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1370ce7d28e3a1f5ed8e56e26bf1ccaf
https://doi.org/10.1016/b978-0-12-821092-5.00006-1
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9
A novel estimation method for capillary pressure curves based on routine core analysis data using artificial neural networks optimized by Cuckoo algorithm – A case study
Autor: Mohsen Hadian, Ramin Moghadasi, Mostafa Mansouri Zadeh, Omid Mohammadzadeh, Majid Jamshidian
Publikováno v: Fuel. 220:363-378
Capillary pressure is one of the most important parameters affecting fluids distribution in a reservoir rock and is an essential input parameter for reservoir simulation. Measurement of capillary pressure data, in the context of special core analysis
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1d10e3d0ce8313f80516cfaa3d18b62a
https://doi.org/10.1016/j.fuel.2018.01.099
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10
A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks
Autor: Mohsen Hadian, W. J. Zhang, Danial Etesami
Publikováno v: Journal of Natural Gas Science and Engineering. 95:104104
Due to the mounting concern in the oil and gas industry about creating well-designed drilling plans and efficiently drilling new wells, analysis of offset well data has received extensive attention over the past few decades. By doing this, bottom hol
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d68e3199b86371521b8c6dc56c530002
https://doi.org/10.1016/j.jngse.2021.104104
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